# Towards Improved Testing For Deep Learning

arXiv: https://arxiv.org/abs/1902.06320 · HTML: https://mlsanity.com/paper/1902.06320/

## Key facts

- Cited by 6 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 17 Feb 2019.

## Built on by (most cited first, 6 of 6)

- [How to Certify Machine Learning Based Safety-critical Systems? A Systematic Literature Review](https://mlsanity.com/paper/2107.12045/) (2021)
- [Black-Box Testing of Deep Neural Networks Through Test Case Diversity](https://mlsanity.com/paper/2112.12591/) (2021)
- [NPC: Neuron Path Coverage via Characterizing Decision Logic of Deep Neural Networks](https://mlsanity.com/paper/2203.12915/) (2022)
- [ArchRepair: Block-Level Architecture-Oriented Repairing for Deep Neural Networks](https://mlsanity.com/paper/2111.13330/) (2021)
- [Beyond Accuracy: An Empirical Study on Unit Testing in Open-source Deep Learning Projects](https://mlsanity.com/paper/2402.16546/) (2024)
- [Robust Black-box Testing of Deep Neural Networks using Co-Domain Coverage](https://mlsanity.com/paper/2408.06766/) (2024)

## Abstract (arXiv)

The growing use of deep neural networks in safety-critical applications makes it necessary to carry out adequate testing to detect and correct any incorrect behavior for corner case inputs before they can be actually used. Deep neural networks lack an explicit control-flow structure, making it impossible to apply to them traditional software testing criteria such as code coverage. In this paper, we examine existing testing methods for deep neural networks, the opportunities for improvement and the need for a fast, scalable, generalizable end-to-end testing method. We also propose a coverage criterion for deep neural networks that tries to capture all possible parts of the deep neural network's logic.